MSCI Inc. ($MSCI)

All right. Good afternoon, everybody. Thank you for being here. For those of you who don't know me, my name is Manav Patnaik. I have a business and information services for Barclays. We're very pleased to kick off our post-line session here with MSCI and we have Alvise Munari, who's the Chief Product Officer, also the Chief -- Head of Client segments, I believe. Alvise, maybe just to start, if you could just maybe give us a little bit of a background, some of -- you're new to a lot of the people in the crowd and in the audience, maybe just a quick bio of how long you've been in MSCI what all you've done and so forth.

Sure. I joined MSCI close to 11 years ago after spending quite amount of time on the sell side working in derivatives. And I initially joined as the Head of EMEA. Then I got promoted to look after our clients globally. That was about 6 years ago. And then about 2 years ago, just standard 2 years ago, I was asked to become the first Chief Product Officer in the history of MSCI. And actually, it's interesting that a company like MSCI had product heads for our different product areas, but never had a single chief product officer. And then about a year ago, I was asked to develop the the segment effort, which is an important transformation in how we think of the opportunity set in the go-to market at MSCI in that we want to focus on understanding a lot more details what clients do in the day-to-day to essentially build more useful tools. And we realized that a dimension that is sufficiently detailed but not too detailed to become clients, individual client specific is to look at basically industry segments. Active asset managers, passive asset managers, pension funds, dominant foundations, wealth managers, hedge funds, so on and so forth. And therefore, we do have now a fully fledged segment strategy. SP1

Got it. Just going back to your point on you're the first Chief Product Officer at MSCI 2 years ago. What was the catalyst for that. I guess there was obviously some deceleration in your business. What was the behind the scenes thoughts on that?

So as the chief of client, I had become very vocal with Henry about 2 challenges. One, that our solutions were not built on an integrated technology stack. And so as a result, they were not easily interoperable. And they were not easily interoperable for us to create further solutions and also for our clients to operate within the stack. So that was challenge number one. And of course, it was clear to me that we were leaving value on the table because of that. We were not helping our clients as much as we could do that. And then the second issue is that I was convinced that we had lost focus on basic power creation discipline. And we did not have a clear function to connect the quantum of product needed to fulfill our growth model with a quantum of new revenues needed from new product quarter-by-quarter. So that was not done in a deliberate disciplined systematic manner. So for those, look, I guess, 2, 3 reasons I said to me, okay, you've been complaining a lot. Go fix it. Okay.

Well, I mean, I guess, credit to you, then maybe it's not a coincidence, but I know on the last call, Henry said that MSCI has introduced more new products in 1Q '26 than they did all of last year. And we've seen some of that momentum in the numbers as well. So what are some of the key initiatives that have led to that?

Look, first of all, I want to clarify 1 thing. So I think -- on the earnings call, Henry might have -- not been very specific or might have gotten slightly carried away. So let's say that either of the two, what is true is that in the first quarter of 2026, we introduced roughly as many new products as in the whole of 2024, okay? And then in 2025, we launched roughly twice as many products as in 2024. And so if you do the simple arithmetic, it would follow that in 2026, we will launch twice as many products as is in 2025, which will be 4x as many product in 2024. And then the reality is that we hope that by the end of the year, our exit velocity in terms of new product launches will bring us to roughly speaking 5x where we were in 2024. That is our aspiration for exit velocity new product creation for...

Got it. And where are all these new products coming from? What are the key areas of focus categories?

Look, very good question. So we are innovating across the board. Because at the end of the day, to grow, to generate new revenues, you need to basically bring to market solutions that basically add additional value, right? So everywhere, we need to innovate. Now -- when I took over the challenge for being the first 3 parks, Okay, well, look, where are we going to innovate first and most aggressively in those areas where there's a combination of 2 things. One, we have done enough work to our technology stack to be able to innovate faster. And two, we understand the usability of the new product better and for the time to mine is going to be quickest. And therefore, we focused on our index business. Why? Because it's obviously the business that MSCI has been in the longest is the 1 which I would say we understand the best holistically, and we have done already a lot of work to the tech stack leading to last year. and therefore, we could innovate faster. And so that's what we did. And therefore, I think you've seen in the last quarter numbers, our index revenue and run rate have shown an acceleration to a large extent because of that. So it's a contribution from new products, okay? Then we focused in areas where the market demand was stronger than usual and we also had a favorable competitive positioning. So that was in the ecosystem centered around private assets. So a lot of innovation there as well. Of course, we bought Burgiss in October 2023. And we then made further smaller but very targeted and specific acquisitions more recently which helped propel our ability to bring to market new solutions a lot faster, new powerful solution a lot faster. And also, did leverage a franchise that MSCI already had in terms of target clients and where we're quite reputationally established, so asset owners. And so in particular, total portfolio solutions. So PPM, which we acquired through the Burgiss acquisition, and then we enhanced through some of the more recent acquisitions, proved to be something we could accelerate the market impact of quite quickly, and we see that accelerating further. So look, it's a very simple number gain. The endowment foundation and single-family office space globally has been very endorses in terms of fit-for-purpose to our portfolio tools. What endowment foundation in single-family office do is nothing other than total portfolio management. I mean, of course, you need to then understand some more specific aspects of what they do asset class by asset class, et cetera, but the most important asking what they do is the management of the total portfolio and the optimization of the total portfolio. So we bring a fit-for-purpose solution, which actually has everything they need in the right place with the right data because we had the private asset data, thanks to the Burgiss acquisition, we parried up with some of the traditional MSCI strength like the factory analytics and all of a sudden, you have literally a best-in-class manning solution. If you add elements of sustainability in Climate, which the asset owner community still is very focused on. All of a sudden, you have something that is believing.

Got it. To your earlier point on the technological limitations, I guess, maybe a 2-part question. Was it AI that helped bridge that gap or some of these new technologies? And then what is the -- you hired a new CTO. So what is the new CTO bring to the table, I guess.

Okay. We hired a new CEO.

Okay, sorry, yes.

We are looking for a new CTO. And so -- let me start with the first part of your question. So there was, on the 1 hand, work that we're already doing in-house, which we continue and accelerated, partly thanks to AI. We did some very successful targeted acquisitions. So Foxberry was in that domain, advantage also in that domain, more recently, Compass, which have literally given us bolt-on capabilities that have augmented the type and quantity of products that we can build and that we can service. And then also, we focused on restarting discharging. So those 4 things together are all necessary, and I think that's what you will see continuing. Now from the point of view of the tech stack and data stack work, I think there was a recognition that MSCI is a financial tool and services company by this financial tools and services literally consists of data models and technology. The model is what we traditionally been excellent at so it's research. The data and the technology had not been as clear and as great of a focus as he had to be. And so Henry took the decision to say, okay, we didn't address somebody focused on the data object and data assets, data architecture and their strategy and going to have somebody really focusing on the product engineering platform and architecture side, and that's the person we're looking for. And so as soon as that happens, and I'm confident it will happen soon. You're going to have an MSCI where the management team is equally focused on all these 3 ingredients as well, of course, as our clients.

Got it. Just touching on AI again, Henry talked about how AI has been a godsend for MSCI. And so from your perspective, can you just talk about some of the different avenues of how AI is improving MSCI I know it sounds like new product innovation, but maybe some other client adoption examples except.

Sure. So Yes, AI is of course, a rich set of tools, a rich set of powerful transformative tools that allow to do knowledge work in a substantially faster more ambitious and more impactful manner at relatively limited cost as you manage it reasonably. Now for a knowledge company like ours, this is revolutionary. That's why Henry calls it godsend. So first of all, the complexity and the scale of the research problems, we can now tackle is literally orders of magnitude more ambitious than what it was 3 years ago. And that, I think, will -- as we get better at mastering new technologies, and we also realize new and different ways that it can improve in too what we're doing. I think this will accelerate okay? So as a resourcer, you can now have 10,000 analysts working for you that can all process data extremely fast, can all read them extremely fast. -- will have access to a vast amount of information that they can excel really quickly and keep the information current. That's unprecedented, okay? So that's -- my view is it for an IP company, it's the most transformational event. Then, of course, there is the aspect of the efficiency, speed, scale and cost. We wish you can build all these new objects and then run them industrially after you actually conceptualize that, right? And of course, various type of AIs are of tremendous hub there. So the quality, scale and size of QA, you can do using machine learning is unprecedented. Now this didn't need LLMs but you can use LLM to our machine learning even more efficiently to do even better QA. So I mean, if you combine the 2, all of a sudden, you can have incredible results. So got better research ideas, you can build them more efficiently. And then the third substantial transformation is that AI can help make it a lot easier for your clients to consume the tool, the content and the services that you produce for them. And that, I think we are just at the beginning of discovery right? Because it's not that this is just like a flick of a switch and everybody is ready to go. Both sides, they took it ready. They need to make certain changes. They need to adopt certain technological protocols and then it will become a lot easier for this to happen. But in some of the more recent solutions that we have launched, of course, the AI empowerment is inbuilt in the solution that we built and then the client just sees the transformation. So we launched this new service called MSCI index AI insights. So at the first level, this is just a way to basically quickly and precisely retrieve information about MSCI vast index IP array, okay? So instead of having to call somebody who then call somebody would then call somebody and get to information. You access the tool, which is available as a stand-alone application to what we call MSCI ONE, but it's also available to MCPs and APIs in both claude and ChatGPT. And my assumption is they will be available in many other ways as well, right? And you get a lot of answers very quickly, right? So figuring out what's going on index, figuring out why the value is not doubt, figuring out what happens with this talked very easy. However, we've done more than that. So there is a very highly trained engine that basically we spent a while honing that you can use to do more sophisticated reasoning. And so you can start asking the engine to do a lot of things that beforehand, you would have had literally a team of people helping you do then calling MSCI, then giving you back the answer they need to rate and then maybe after 2 weeks get what you need it. Now you can actually do in the matter of minutes.

Got it. One of the buzzwords being used as MCP and a lot of companies are trying to provide stats on their partnerships, et cetera. How do you look at your current relationship with the LLM, -- like what kind of revenue model, business model are you now? And where do you think it eventually evolves into?

Yes. Look, I think we're at the very beginning of the journey, right? So as it's always the case at the beginning of our transformation, you don't know exactly how the new world will be structured. You know exactly what patterns or protocols will prevail at scale versus which 1 or not. But I think it's a fair bet to assume that at least for the largest users of our content and services, they will want a programmatic ways to interact with it. They already do. So by the way, we've been using APIs like many of our peers, competitors and larger clients already for 10-plus years, okay? The APIs were not architecture in the way you need to have now to make everything easily AI fungible, but the concept is not new. Now API's personal CP is different because basically say, okay, I'm going to provide like a universal only [indiscernible] guy that tells you exactly how to use and where to find myself in a way that basically means that you don't have to actually spend a whole lot of time figuring out how to go via the APIs. So that's a transformation. And then if both sides develop agentic capabilities that are probable through the -- through the APIs, I think that's where the real transformation begins, right? And I think that's what we had at the early stages of. To be honest, right now, we're seeing more direct tangible client impact. On the capabilities that we have identified and make available to them by our applications then via MCP API. So for instance, MSCI Index Insights we have many, many, many more users that come to MSCI ONE that ones that go to the cloud MCPs. We do it if you to go to the cloud MCP. I mean a few means, probably a few hundred, but we do have many, many more through MSCI ONE. Same thing with many of our more complex tools and services. For the time being, the client base tellers, look, if you go to your own thing, we are more comfortable. We have -- we are more sure that we're going to get the right answer. We're going to get the right reasoning. And so for the time being, we prefer to use that. Is that going to change? Look, it depends what you're using the engine for. If it's just retrieval and then you use your own logic, then I think you want to go to generic APIs and MCPs. If you're a very large-scale players, all you're going to focus on is the retrieval bit -- and I think anything you're going to go through that. If you are not such a scale player and so you want to benefit from the specialized purpose-built reason in MSCI provide, I think you're going to go to our channels. Now your point about pricing models and the evolution of the economic curve. Look, any way this is going to shape up. it's clear that he will get people to call more data, more services, more functionality, more tests, more valid scenarios because there is a possibility to do it. And a lot of these things is useful. -- they are useful, right? So people didn't used to do it because he was very complicated. So you just really asked what you couldn't figure out yourself. But as it becomes set, you're going to say, sure, why do I want an answer which is directionally correct. I want the exact answer. I'm going to ask the question. I'm going to do it programmatically, and I can ask 1,000 questions, handle 1,000 questions, million question, 100 million question, right? So -- the way I think this is going to evolve is that we're going to have a layered pricing models, whereby you're going to have access to a certain set of modules of data and functionalities depending on what you decided to pay for. There will be then a consumption layer. -- okay, where there'll be a basic consumption pattern included in the basic fees. You ask and questions today. We're not going to charge you more money. You ask a 1,000 question a day -- maybe we start thinking about it. You start you ask 50,000 questions a day, we push for charging more money, right? Because we'd have to support other trial, right? And so there will be charges for that. Then there will be the question of whether you actually ask the engines to do bespoken audits for you, simulate a portfolio, do a stress test, try out a different score of this type of that type, build a customer index, build a capital model, okay? And so we'll charge you by order. We can decide to build a new customer index. We'll try to sell out to money. You decide to basically run many of it. And there is a clear relationship on how you're going to run the money. We'll charge you ABF like we do today, if you are somebody who's licensing and indexed to ETF. So I think that's going to be the evolutionary curve.

Got it. You mentioned custom indices, so let's touch on that. I think historically, people into change custom indices with self-indexing and they thought that they would be the depth of all the index providers that never happen -- you guys have grown your custom indices business in the low double-digit rate for a long time. And last quarter, it was into the 20%. So can you just tell us what's been driving the good growth, what drove the incremental growth in first quarter?

Okay. So -- there is, I think, a long-term trend, and there is specific events -- so the significant growth in growth that you saw last quarter is driven by both. So on the 1 hand, we do have a strong long-term trend, which I can talk about in a second. And then we also had a very substantial licensing event with a big investment house that basically decided to substantially increase the amount of IP license from us in that space that help sales. Now that did not happen in a vacuum, okay? So we see that as literally as the logical manifestation of the long-term trend, what is the long-term trend. The long-term trend is that the financial industry wants more and more portfolio of recipes. Why does he want more and more portfolio recipes because like no 2 human beings, no 2 institutions have the same portfolio objectives. They have similar KPIs potentially. They have similar challenges to tackle, but no 2 of them want exactly the same portfolio object. And of course, because it really was not possible to build them truly customized and supportable individualized portfolio solution. We can do it. We found a sort of proxies, people used to have in a few of-the-shelf model portfolio options to offer to their clients, few versions of the same basic building blocks and now various combination of this. Now same with indices, okay? MSCI built his reputation, building benchmarks. And the philosophy was a benchmark for all, okay? It's important. A benchmark for all. So basically, everybody was essentially the same KPIs should be happy with the same benchmark, right? Because you're starting to say, "Okay, what's the right way of doing x y z, here is the benchmark that tells you exactly what is the right way to do x y z. And So if you want to understand whether you're getting the right service, not the right service, where you should go a bit more left, a bit more right, you look at the benchmark and you see how much and why you're deviating from the benchmark, okay? Say, okay, great. So we have all figured out the ideal ways to do certain things, but let's face it, none of us is that ideal person. None of us need the stylized benchmark portfolio, we need different things. And so you say, okay, well, can index technology help me to literally formalize the expression or what I need specifically for my personalized portfolio? And the answer is, of course, it can. In fact, it's the best technology available in the world to do that, right? Because you can literally encode all the KPIs needed by the individual investor, institutional or retail into our programmatic set of steps. There you can then feed to a computer and of course, propagate forever unless the KPIs need to change in which case, you can adapt them. And be sure that as long as the KPIs are still the 1 the clients need the investment recipe will do the job the right way, okay? And so both say, "Wow, why shouldn't we do that?" and that's exactly what has been happening. So the technology has progressed, first slowly then much more rapidly to support this. People have understood first little and then more apply that this can be applied in all sort of context. By the way, including in supporting active asset management. And therefore, that's what you're seeing. You're seeing that in all context of investment and financial side people are starting to look at this. So whether it's in the context of building model portfolio for individual wealth management clients in the context of building multi-asset class benchmark for us at over as insurance companies in the context of building bespoke benchmark for investment institutions that want to make sure that PM's doing the job really as best as they could whether it is for the more traditional applications, ETF, whether it is to build structure products, whether it's to build customized systematic investment recipes for pension funds or hedge funds. You can do it by an index.

Got it. Maybe just a quick follow-up. Like in the indices business, especially on the custom side, I think Foxberry, you mentioned was an acquisition you made recently acquired Compass as well -- what did those 2 bring to the table because the impression is you guys index powerhouse

So as I said. MSCI built its reputation in its business on the benchmark for all year, okay? Now many components of the infrastructure that you need to support indexation and service a large -- we're not natively built during the benchmark for all year. Best-in-class corporate action engine, best-in-class way to calculate including actions have to do with free float, market structure and so on and so forth. And a whole host of other things, similar to this, right? When it came to enabling very specialized custom index portfolio construction recipe, the engine has not been built for that, right? Foxberry has been built exactly for that. So that's why we bought -- so as, okay, we have the best-in-class central engine that does all these things that are needed at scale for a standardized massive index business. We have the calculation engine that is very robust and reliable. We need to improve the portfolio construction engines we bought Foxberry, we split in, and that's how you actually get a massive acceleration in the custom equity index business. Now again, MSCI was built to do benchmark for all, especially in equities, especially in equities, right? So we entered the fixed income business 5 or 6 years ago. We're now growing there. it's a very attractive proposition, but let's face it. We did not have the same capabilities. And so we started working with Compass to satisfy needs from for instance, structural product market, where people say, "Gee, guys, you are so good at equities, especially after you bought Foxberry but we see demand for multi-asset cluster, okay? We see demand for nonlinear payoffs that embed option component, futures component. And we were doing that, but we could do it only small scale. So I started working with Compass. We saw that they were truly excellent. They have built a really impressive engine that could do this at scale with a high degree of flexibility for all asset classes, all type of payoffs, so long, short option-based, future-based, option and future based. They also handle commodities and cryptocurrencies. And so we said, okay, maybe we should buy them. And so with it.

Got it. Okay. That's super helpful. So appreciate that. Maybe let's just touch on the 2 other deals you did recently as well, Vantage and PM Insights. I believe they both fall into your private assets category where Burgiss is obviously your main assets. What are those assets? How do they fit into the plans there?

Yes. So Vantage, we bought because of the private asset business, but also because the capability they develop, we think, will be really helpful for us to have and then further develop across the board. So what manager had developed is a highly capable, highly specialized AI-powered engine for data extraction to a model in the context of private assets documentation, basically, take a whole bunch of PDF, a carload and basically engine to fit a specific data model, you precreated in a very dependable and competent way. So you can actually turn millions of PDF pages into very highly conceptualized structural data that you can then use in the investment process, that's a board, right? This is, of course, immediately super helpful in our private asset business, but we think it's going to be helpful across the board. Why? Well, because -- look, we think that the future of the investment industry -- on the 1 hand, we'll keep on being about analyzing time series. But on the other hand, it will be about building very specific knowledge about individual assets. Some of it is done by the traditional financial analyst process, which does a fantastic job. A lot of it will need to be enriched by looking for all sorts of data sources in all sorts of structure forms with this type of capabilities will be critical to build the IPs, okay? And so PMI, look, we got to know PMI because [indiscernible], we still are the benchmark guys, right? So we build all the possible benchmark that 1 could ever think of in the equity space. We now -- we're now doing it in fixed income, although in fixed income, there's already other people who we find ways to differentiate ourselves, which we're doing. And then we say, okay, we're going to do it across public asset classes, thanks to the acquisition of Compass and then we say, what about the private assets, right? And in private assets, we've indeed started to build systematically fit-for-purpose bench works for all asset classes, sub-asset classes, different type of cuts, different type of regions and so on and so forth. However, those objects, they have the liquidity limitation that the underlying asset has. So if you want to start bridging the gap between public asset bench and private asset bench, you need to try and find something in between. So PMI had developed a very powerful technology based on having all sorts of agreements with industry players in the secondary market to be able to essentially estimate evaluated prices. And so we built with them a peaking venture index. That worked out by well. So we said, okay, well, we like to do this more broadly. And so was looking, when we buy them, right? And so indeed, this will accelerate our ability to build really usable, in this case, not bantering really indices for the private asset industry. First, on the equity side, on the credit side, we are doing it, by the way, also. We're doing it in a different way. So there'll be something hitting the news soon, hopefully, there as well. But as we heard, I know how many of you heard the launch presentation from Arris, which I thought actually was really interesting. At the end of the day, what are investors asking them is how do you get your marks? Okay? How are you going -- and look, in private credit, I believe that probably the answer that someone like ours would give their investors may not be perfect, but would be reasonably solid. Probably, we can do better because that's what we do for a living, but they are reasonably solid. In the private equity space, I think there's a lot of work to be done, right? And so we aim to bring transparency to the world, make it easier to start seeing the -- in comparing the returns across all ways of holding equities and, therefore, help the investor community be more effective in how they allocate capital.

Got it. Okay. We've got 5 minutes left. So let me focus on analytics. So when it comes to indices, I think consensus is, it's a benchmark probably not disruptible by AI, private assets is evolving. But on the analytics side, can you help us appreciate what the what the moat there is versus all these AI solutions that could potentially threaten that?

So look, the analytics business is a vast business. So there certainly are aspects of what people do, including ourselves in analytics that are substantially more prone to AI disruption, and there are other parts of what people refer to typically as analytics that are substantially harder to disrupt, okay? So let's talk about what is easy to disrupt. So anything that has to do with workflow is up. And even easier, if the workflow is not particularly proprietary, not particularly unique, not particularly insightful into what specific client or type of clients are doing, okay? And so somebody was providing relatively standard risk of performance attribution on liquid public assets. That's going to get disrupted okay? At the other end of the spectrum is where you are building proprietary risk and performance models based on 40 years plus of very granular data that has been clients in an extremely disciplined and pedantic, but fit-for-purpose manner across a multisite markets that you can, therefore, use to understand the structure of your portfolio exposure across the board in a basically single coherent manner, that's not so easy to disrupt. Now if you then say -- also say, okay, let's think about the index benchmarking analogy. So we built the benchmark. The benchmark essentially a standard. The standards are useful because not only do they tell you what the theoretical performance of a certain objects would be, but they also help you crystallize norms that are going to be useful even if you do totally freestyle portfolio construction, okay? Same with models. So enter new technologies, and we can now build new models and calibrate models in a way that it customize to clients in a matter of hours, use a pickup months. All of a sudden, we can innovate a lot more in the model space as well, okay? So my view is, therefore, that is going to be the opportunity to actually extend the mode, thanks to AI as opposed to seeing the old get a order because of AI, okay? So I think it's a question of where you are in this spectrum. So the model capabilities are the hardest to replicate the very generic risk and performance analysis are the easiest to disrupt. The total portfolio services may get easier to disrupt, but there is a lot of ingredients you need to understand how to put together in a coherent systematic and dependable manner. So I think they are a lot closer to the far end of the model side than the other end.

So I guess MSCI makes us more to the fire end. Is that what you're saying?

Well, what I'm saying is that, look, I think that there is a way to do all of this in more intelligent and more value-additive ways using AI, if you understand the finance first and foremost, and you understand the client. But in particular, within the model and total portfolio solutions space, I think the advantage we have is so substantial that it will be very difficult for a very smart engine to actually replace us anytime soon.

Okay. In the interest of time, 1 last question, 30-second answer -- everyone talks about the slow decline of the asset managers and that requires its own fireside chat probably. But you've been growing a ton on the trading and hedge fund side. How early are we in that segment penetration process. Could that keep growing to help offset that.

Listen, I told people, look, if you look at our large-scale trading operations and whether it's the trading operations of broker dealer, so it's officially called the trading operation or whether it's the trading operation of a propriety market maker, so is called the market and making operations. A trading operation of a large platform hedge fund so is called the investment desk called the pods. The point is they all do equities and fixed income. Within equities, they do idiosyncratic linear, systematic linear. No, nonlinear. So typically after involving 1 or more broad-based strategies, and they do much more complex relative value, okay? Right now, we play a significant role in equity, linear systematic -- so we've got plenty of adjacent areas to capital. Now what do I think is going to happen, okay? The world of investment and, therefore, of trading and therefore, all of finance is converging decisively to a portfolio-centric architecture, okay? The unit of conceptual exchange will be not the individual idiosyncratic risk will be the portfolio. I mean, of course, some people need to slice and dice idiosyncratic of course, you do this for a living, right? But the reality is the majority of people well understand portfolio. They won't even want to touch the individual exposure, right? In fact, it's probably best for everybody that the vast majority of people never touch it, right? In that journey, you need somebody that basically offers -- everybody can agree around and therefore communicate with that's what MSCI aim to do across all asset classes, all formal portfolio trading, linear, non-linear, convex, non-convex around the whole globe.

Got it. All right. Cool. We're out of time there. Thank you so much. Thank you, everyone. Thank you.